Metadata-Version: 2.1
Name: nuset-lib
Version: 0.2.0
Summary: UNKNOWN
Home-page: UNKNOWN
License: MIT License
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/markdown
Requires-Dist: Pillow
Requires-Dist: numpy
Requires-Dist: requests
Requires-Dist: scikit-image
Requires-Dist: tensorflow
Requires-Dist: tqdm

# nuset-lib
[![PyPI version](https://badge.fury.io/py/nuset-lib.svg)](https://badge.fury.io/py/nuset-lib)

NuSeT packaged as a library with an easy to use API

`nuset-lib` is based on the NuSeT package by Linfeng Yang: https://github.com/yanglf1121/NuSeT

Their paper:  https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008193

Please cite their paper if you use `nuset-lib`

Training is not yet implemented but it can be used for predicting.

## Installation

`nuset-lib` can be installed via `pip`.

```
pip install nuset-lib
```

## After installation
~1GB of network weights will be downloaded the first time that you import `nuset`. 
By default these network weight files are kept in your user home directory. 
If you do not want these files to be stored in your home directory (such as with shared computing systems, limited user quotas etc.), 
you may specify a different location by setting the following environment variable:

```
export NUSET_CONFIG=/path/to/dir
```

**On RTX 2000 series cards you will need to set the following environment variable due to a bug in tensorflow:**

```
export TF_FORCE_GPU_ALLOW_GROWTH=true
```

## Basic Usage

```python
from nuset import Nuset
import numpy as np
from matplotlib import pyplot as plt
import tifffile

img = tifffile.imread('path to file')

nuset = Nuset()

mask = nuset.predict(
    image=img,
    watershed=True,
    min_score=0.8,
    nms_threshold=0.1,
    rescale_ratio=2.5
)

thr = 0.7

mask[mask < thr] = 0
mask[mask > thr] = 1

fig = plt.figure(figsize=(15, 15))

plt.imshow(img, cmap='viridis')
plt.imshow(mask, alpha=0.5)
plt.show()

```

You may benefit from preprocessing the image to adjust gamma, equalize the histogram etc.

See the example notebook for more details: https://github.com/kushalkolar/nuset-lib/blob/master/example.ipynb


